An individual wishing to find a piece of text that contains content similar to the content of another piece of text will often search for words that exist in both pieces. Likewise, an individual wishing to find relevant pieces of text will often search for specific words that are thought likely to indicate relevant content in text.
These techniques are central to methods for searching electronic records for relevant text but are also used for searching analog records. Examples of the latter would include concordances and library subject catalogs.
The limitations of existing methods are several: 1) words, and especially acronyms, may have multiple meanings; 2) large records, such as lengthy books and encyclopedias, might contain the specific words themselves but be largely irrelevant to the content sought or contain only a small section of relevant content in a much larger document; 3) text using synonyms of the specific words used for searching might not be found; and, 4) text written in foreign languages will be largely inaccessible.
In general, since there are many ways of expressing the same thought, words representing snippets of the text are not the ideal thing to look for. There is a compelling need for a user to find text that contains the meaning and thoughts that the user is looking for. There is a compelling need for a user to search and find relevant textual content that does not suffer from the drawbacks of the prior art and that allows a user who desires to find relevant content, and who tells the computer to look for some particular text, to infer the meaning of that text rather than to conduct a search that is confined to the literal words of the text themselves.
When a user searches for a keyword, advertisers want to post advertisements next to the results of the search request. Advertisers bid on particular combinations of words and will pay to have their advertisements placed high on the search results page. The search engine gets paid when users open one of the advertisements of the advertiser. If the advertisement is relevant to the user's actual interest, the user is much more likely to click on and open up an advertisement and thereby generate revenues for the search engine. For example, if a user is searching for information concerning a news story relating to a murder that was in the news the user might type in the following text as a search request: “Laci Peterson's body washed ashore”. An advertiser who is selling body wash does not want to waste his advertisement on user's who placed that search. Moreover, a search engine does not want to lose revenues from users who do not click on an advertisement due to the advertisement being of low relevance and interest to the user who sees it. There is therefore a compelling need for search engines and advertisers to find text that contains the meaning and thoughts that the user is looking for.
Current methods available to advertisers fail to detect and respond to sudden changes in the online community's topics of interest. Hence, advertisers are often unable to adjust their advertising campaigns to meet this sudden demand for information (and related products and services) connected with these surges in interest. Furthermore, these surges in interest can occur over discrete geographic areas within the overall theater of an advertiser's campaign. As can be seen, there is a need for a method and system that can immediately detect changes in the online community's interests to enable advertisers to respond in real time to such changes by adjusting their advertising campaigns or by tailoring such campaigns to specific geographic areas.
In addition, pharmaceutical companies are known to have a compelling need to keep abreast of the side effects of drugs both before and after the drug has entered the market. Doing so can be costly and time-consuming since they may involve double blind clinical studies. New side effects cannot easily be searched on the Internet since the side effects are not yet known. Furthermore, searching the Internet or the blogosphere for all information about a particular drug would simply turn up too much information. It has been reported that if the drug company Merck had reports of partially used prescriptions for Vioxx®, it would have been useful for it to stay on top of the developments of reported side effects for Vioxx®, its drug.
As can be seen there is a need for more effective ways for pharmaceutical companies to monitor the side effects of the pharmaceuticals they sell.